Support val_check_interval values higher than number of training batc…

…hes (Lightning-AI#11993)

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -481,7 +481,7 @@ def _should_check_val_fx(self, batch_idx: int, is_last_batch: bool) -> bool:
         if not self.trainer.enable_validation:
             return False
 
-        is_val_check_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
+        is_val_check_epoch = self.trainer.check_val_every_n_epoch is None or ((self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0)
         if not is_val_check_epoch:
             return False
 
@@ -498,7 +498,13 @@ def _should_check_val_fx(self, batch_idx: int, is_last_batch: bool) -> bool:
         if isinstance(self.trainer.limit_train_batches, int) and is_infinite_dataset:
             is_val_check_batch = (batch_idx + 1) % self.trainer.limit_train_batches == 0
         elif self.trainer.val_check_batch != float("inf"):
-            is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0
+            # if `check_val_every_n_epoch is `None`, run a validation loop every n training batches
+            # else condition it based on the batch_idx of the current epoch
+            current_iteration = (
+                self._batches_that_stepped if self.trainer.check_val_every_n_epoch is None else batch_idx
+            )
+            is_val_check_batch = (current_iteration + 1) % self.trainer.val_check_batch == 0
+
         return is_val_check_batch
 
     def _save_loggers_on_train_batch_end(self) -> None:

pytorch_lightning/trainer/connectors/data_connector.py

@@ -71,8 +71,9 @@ def _should_reload_val_dl(self) -> bool:
 
     def on_trainer_init(
         self,
-        check_val_every_n_epoch: int,
+        val_check_interval: Union[int, float],
         reload_dataloaders_every_n_epochs: int,
+        check_val_every_n_epoch: Optional[int],
         prepare_data_per_node: Optional[bool] = None,
     ) -> None:
         self.trainer.datamodule = None
@@ -85,9 +86,15 @@ def on_trainer_init(
             )
         self.trainer.prepare_data_per_node = prepare_data_per_node
 
-        if not isinstance(check_val_every_n_epoch, int):
+        if check_val_every_n_epoch is not None and not isinstance(check_val_every_n_epoch, int):
             raise MisconfigurationException(
-                f"check_val_every_n_epoch should be an integer. Found {check_val_every_n_epoch}"
+                f"`check_val_every_n_epoch` should be an integer, found {check_val_every_n_epoch!r}."
+            )
+
+        if check_val_every_n_epoch is None and isinstance(val_check_interval, float):
+            raise MisconfigurationException(
+                "`val_check_interval` should be an integer when `check_val_every_n_epoch=None`,"
+                f" found {val_check_interval!r}."
             )
 
         self.trainer.check_val_every_n_epoch = check_val_every_n_epoch

pytorch_lightning/trainer/trainer.py

@@ -148,7 +148,7 @@ def __init__(
         enable_progress_bar: bool = True,
         overfit_batches: Union[int, float] = 0.0,
         track_grad_norm: Union[int, float, str] = -1,
-        check_val_every_n_epoch: int = 1,
+        check_val_every_n_epoch: Optional[int] = 1,
         fast_dev_run: Union[int, bool] = False,
         accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None,
         max_epochs: Optional[int] = None,
@@ -248,7 +248,9 @@ def __init__(
                 :paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`.
                 Default: ``True``.
 
-            check_val_every_n_epoch: Check val every n train epochs.
+            check_val_every_n_epoch: Perform a validation loop every after every `N` training epochs. If ``None``,
+                validation will be done solely based on the number of training batches, requiring ``val_check_interval``
+                to be an integer value.
                 Default: ``1``.
 
 
@@ -427,7 +429,8 @@ def __init__(
 
             val_check_interval: How often to check the validation set. Pass a ``float`` in the range [0.0, 1.0] to check
                 after a fraction of the training epoch. Pass an ``int`` to check after a fixed number of training
-                batches.
+                batches. An ``int`` value can only be higher than the number of training batches when
+                ``check_val_every_n_epoch=None``.
                 Default: ``1.0``.
 
             enable_model_summary: Whether to enable model summarization by default.
@@ -561,8 +564,9 @@ def __init__(
         # init data flags
         self.check_val_every_n_epoch: int
         self._data_connector.on_trainer_init(
-            check_val_every_n_epoch,
+            val_check_interval.
             reload_dataloaders_every_n_epochs,
+            check_val_every_n_epoch,
             prepare_data_per_node,
         )
 
@@ -1862,11 +1866,12 @@ def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -
 
         if isinstance(self.val_check_interval, int):
             self.val_check_batch = self.val_check_interval
-            if self.val_check_batch > self.num_training_batches:
+            if self.val_check_batch > self.num_training_batches and self.check_val_every_n_epoch is not None:
                 raise ValueError(
                     f"`val_check_interval` ({self.val_check_interval}) must be less than or equal "
                     f"to the number of the training batches ({self.num_training_batches}). "
                     "If you want to disable validation set `limit_val_batches` to 0.0 instead."
+                    "If you want to validate based on the total training batches, set `check_val_every_n_epoch=None`."
                 )
         else:
             if not has_len_all_ranks(self.train_dataloader, self.strategy, module):